COMPARISON BETWEEN HAAR WAVELET TRANSFORM, DCT AND A PROPOSED COLUMN- MEAN- METHOD BASED IRIS ENCODERS

Ibrahim Zedan, Mira M. Sobhi

Abstract


Human iris is one of the most reliable biometrics because of its uniqueness, stability and noninvasive nature. Thus it has attracted the attention of biometrics based identification and verification research and development community. Iris recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of iris recognition algorithms have been developed for decades. This paper presents a comparative study of the performance from the iris authentication using Discrete Cosine Transform (DCT), Haar wavelet transform and a proposed Column Mean Method based features. Here iris recognition is done using the image feature set extracted from DCT, Haar Wavelet transform and Column Mean. Analysis was performed with the mentioned methods, consisting of the False Acceptance Rate (FAR) and the Genuine Acceptance Rate (GAR). The proposed technique is tested on an iris image database having 384 images. The iris recognition systems that produce very low error rates were successfully designed. DCT and Column Mean Methods give better performance with the accuracy of 98.44% and Haar Wavelet Transform gives a performance with the accuracy of 97.66%. The proposed Column Mean Method is superior in having faster recognition rate

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